


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					Optional[int]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					Union[str, Any]      =       {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_SCREAMING_SNAKE_CASE     :					Tuple      =       [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     for attribute in key.split('''.'''							):
          snake_case											=							getattr(UpperCamelCase_				,UpperCamelCase_							)

     if weight_type is not None:
          snake_case											=							getattr(UpperCamelCase_				,UpperCamelCase_							).shape
     else:
          snake_case											=							hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
              F''' {value.shape} for {full_name}'''							)

     if weight_type == "weight":
          snake_case											=							value
     elif weight_type == "weight_g":
          snake_case											=							value
     elif weight_type == "weight_v":
          snake_case											=							value
     elif weight_type == "bias":
          snake_case											=							value
     else:
          snake_case											=							value

     logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							[]
     snake_case											=							fairseq_model.state_dict()

     snake_case											=							hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          snake_case											=							False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,hf_model.config.feat_extract_norm == '''group'''				,)
               snake_case											=							True
          else:
               for key, mapped_key in MAPPING.items():
                    snake_case											=							'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('''.'''							)[:-1]							) != key):
                              # special case since naming is very similar
                              continue
                         snake_case											=							True
                         if "*" in mapped_key:
                              snake_case											=							name.split(UpperCamelCase_							)[0].split('''.'''							)[-2]
                              snake_case											=							mapped_key.replace('''*'''				,UpperCamelCase_							)
                         if "weight_g" in name:
                              snake_case											=							'''weight_g'''
                         elif "weight_v" in name:
                              snake_case											=							'''weight_v'''
                         elif "bias" in name:
                              snake_case											=							'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              snake_case											=							'''weight'''
                         else:
                              snake_case											=							None
                         set_recursively(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCamelCase_							)

     logger.warning(F'''Unused weights: {unused_weights}'''							)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							full_name.split('''conv_layers.'''							)[-1]
     snake_case											=							name.split('''.'''							)
     snake_case											=							int(items[0]							)
     snake_case											=							int(items[1]							)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
               snake_case											=							value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
               snake_case											=							value
               logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''							)
               snake_case											=							value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'''{full_name} has size {value.shape}, but'''
                        F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''							)
               snake_case											=							value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
     else:
          unused_weights.append(UpperCamelCase_							)
@torch.no_grad()
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_=None				,UpperCamelCase_=None				,UpperCamelCase_=True							):



     """simple docstring"""






     if config_path is not None:
          snake_case											=							UniSpeechSatConfig.from_pretrained(UpperCamelCase_							)
     else:
          snake_case											=							UniSpeechSatConfig()

     snake_case											=							''''''

     if is_finetuned:
          snake_case											=							UniSpeechSatForCTC(UpperCamelCase_							)
     else:
          snake_case											=							UniSpeechSatForPreTraining(UpperCamelCase_							)

     snake_case					,    snake_case					,    snake_case											=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]				,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
     snake_case											=							model[0].eval()

     recursively_load_weights(UpperCamelCase_				,UpperCamelCase_							)

     hf_wavavec.save_pretrained(UpperCamelCase_							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 _SCREAMING_SNAKE_CASE     :					int      =       parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       ["SpeechEncoderDecoderModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Dict      =       ["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
 from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
 import sys

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




